from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 1s
KMeans_short: 0h 0m 2s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 10s
LogisticRegression: 0h 0m 19s
KMeans_tall: 0h 0m 21s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s
daal4py_KNeighborsClassifier: 0h 2m 35s
KNeighborsClassifier_kd_tree: 0h 2m 41s
catboost_lossguide: 0h 5m 3s
catboost_symmetric: 0h 5m 6s
lightgbm: 0h 5m 11s
xgboost: 0h 5m 18s
HistGradientBoostingClassifier: 0h 5m 19s
KNeighborsClassifier: 0h 31m 33s
total: 1h 4m 30s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.135 | 0.000 | 5.927 | 0.000 | 1 | 1 | NaN | NaN | 0.455 | 0.000 | 0.297 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 12.171 | 0.040 | 0.000 | 0.012 | 1 | 1 | 0.708 | 0.721 | 1.801 | 0.027 | 6.757 | 0.103 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.177 | 0.001 | 0.000 | 0.177 | 1 | 1 | 1.000 | 1.000 | 0.082 | 0.001 | 2.174 | 0.031 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.126 | 0.000 | 6.353 | 0.000 | 1 | 100 | NaN | NaN | 0.438 | 0.000 | 0.287 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.510 | 0.109 | 0.000 | 0.021 | 1 | 100 | 0.931 | 0.815 | 1.833 | 0.027 | 11.187 | 0.174 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.186 | 0.001 | 0.000 | 0.186 | 1 | 100 | 1.000 | 1.000 | 0.083 | 0.001 | 2.222 | 0.038 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.160 | 0.000 | -1 | 100 | NaN | NaN | 0.444 | 0.000 | 0.292 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 31.800 | 0.000 | 0.000 | 0.032 | -1 | 100 | 0.931 | 0.944 | 1.906 | 0.028 | 16.687 | 0.244 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.162 | 0.014 | 0.000 | 0.162 | -1 | 100 | 1.000 | 1.000 | 0.083 | 0.001 | 1.961 | 0.167 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.125 | 0.000 | 6.402 | 0.000 | -1 | 5 | NaN | NaN | 0.440 | 0.000 | 0.284 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 31.564 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.801 | 0.721 | 1.828 | 0.035 | 17.263 | 0.332 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.159 | 0.013 | 0.000 | 0.159 | -1 | 5 | 1.000 | 1.000 | 0.082 | 0.001 | 1.955 | 0.162 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.123 | 0.000 | 6.520 | 0.000 | -1 | 1 | NaN | NaN | 0.438 | 0.000 | 0.280 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.931 | 0.251 | 0.000 | 0.024 | -1 | 1 | 0.708 | 0.815 | 1.882 | 0.042 | 12.716 | 0.312 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.167 | 0.017 | 0.000 | 0.167 | -1 | 1 | 1.000 | 1.000 | 0.083 | 0.002 | 2.012 | 0.208 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.124 | 0.000 | 6.472 | 0.000 | 1 | 5 | NaN | NaN | 0.443 | 0.000 | 0.279 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 20.740 | 0.055 | 0.000 | 0.021 | 1 | 5 | 0.801 | 0.944 | 1.943 | 0.017 | 10.672 | 0.096 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.002 | 0.000 | 0.187 | 1 | 5 | 1.000 | 1.000 | 0.086 | 0.008 | 2.182 | 0.200 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.276 | 0.000 | 1 | 1 | NaN | NaN | 0.094 | 0.000 | 0.620 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.737 | 0.051 | 0.000 | 0.010 | 1 | 1 | 0.966 | 0.977 | 0.296 | 0.007 | 32.861 | 0.748 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.000 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 2.652 | 0.095 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.276 | 0.000 | 1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.598 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.897 | 0.090 | 0.000 | 0.020 | 1 | 100 | 0.980 | 0.983 | 0.281 | 0.014 | 70.915 | 3.541 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.037 | 0.183 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.278 | 0.000 | -1 | 100 | NaN | NaN | 0.092 | 0.000 | 0.626 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.239 | 0.000 | 0.000 | 0.030 | -1 | 100 | 0.980 | 0.983 | 0.323 | 0.006 | 93.577 | 1.668 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.001 | 0.000 | 0.024 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.283 | 0.223 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.309 | 0.000 | -1 | 5 | NaN | NaN | 0.093 | 0.000 | 0.557 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.804 | 0.000 | 0.000 | 0.031 | -1 | 5 | 0.977 | 0.977 | 0.277 | 0.010 | 111.379 | 3.870 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.002 | 0.000 | 0.025 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 4.344 | 0.408 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.276 | 0.000 | -1 | 1 | NaN | NaN | 0.095 | 0.000 | 0.612 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.017 | 0.174 | 0.000 | 0.021 | -1 | 1 | 0.966 | 0.983 | 0.284 | 0.002 | 74.094 | 0.869 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.002 | 0.000 | 0.019 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 3.363 | 0.440 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.305 | 0.000 | 1 | 5 | NaN | NaN | 0.094 | 0.000 | 0.556 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 18.190 | 0.159 | 0.000 | 0.018 | 1 | 5 | 0.977 | 0.983 | 0.342 | 0.007 | 53.195 | 1.223 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 3.661 | 0.220 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.088 | 0.000 | 0.026 | 0.000 | -1 | 1 | NaN | NaN | 0.737 | 0.000 | 4.188 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.438 | 0.008 | 0.000 | 0.000 | -1 | 1 | 0.970 | 0.974 | 0.605 | 0.009 | 0.724 | 0.017 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 2.325 | 0.687 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.339 | 0.000 | 0.024 | 0.000 | 1 | 1 | NaN | NaN | 0.703 | 0.000 | 4.750 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.747 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.970 | 0.980 | 0.194 | 0.002 | 3.856 | 0.044 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.642 | 1.123 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.190 | 0.000 | 0.025 | 0.000 | 1 | 5 | NaN | NaN | 0.743 | 0.000 | 4.291 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.462 | 0.014 | 0.000 | 0.001 | 1 | 5 | 0.979 | 0.974 | 0.597 | 0.017 | 2.449 | 0.075 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.703 | 0.660 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.160 | 0.000 | 0.025 | 0.000 | -1 | 100 | NaN | NaN | 0.710 | 0.000 | 4.453 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.759 | 0.017 | 0.000 | 0.003 | -1 | 100 | 0.978 | 0.968 | 0.110 | 0.001 | 25.172 | 0.280 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.009 | 0.001 | 0.000 | 0.009 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 26.653 | 11.287 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.271 | 0.000 | 0.024 | 0.000 | -1 | 5 | NaN | NaN | 0.698 | 0.000 | 4.684 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.847 | 0.007 | 0.000 | 0.001 | -1 | 5 | 0.979 | 0.980 | 0.196 | 0.004 | 4.319 | 0.097 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.000 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 9.259 | 3.825 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.309 | 0.000 | 0.024 | 0.000 | 1 | 100 | NaN | NaN | 0.698 | 0.000 | 4.737 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.814 | 0.051 | 0.000 | 0.005 | 1 | 100 | 0.978 | 0.968 | 0.108 | 0.002 | 44.661 | 1.092 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.000 | 0.000 | 0.007 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 19.393 | 7.225 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.856 | 0.000 | 0.019 | 0.000 | -1 | 1 | NaN | NaN | 0.487 | 0.000 | 1.759 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.970 | 0.978 | 0.007 | 0.000 | 3.519 | 0.155 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 17.148 | 12.433 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.860 | 0.000 | 0.019 | 0.000 | 1 | 1 | NaN | NaN | 0.490 | 0.000 | 1.756 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.970 | 0.980 | 0.001 | 0.000 | 21.805 | 6.063 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.085 | 3.896 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.876 | 0.000 | 0.018 | 0.000 | 1 | 5 | NaN | NaN | 0.485 | 0.000 | 1.807 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.988 | 0.978 | 0.007 | 0.000 | 3.608 | 0.174 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.565 | 3.386 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.862 | 0.000 | 0.019 | 0.000 | -1 | 100 | NaN | NaN | 0.476 | 0.000 | 1.812 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.046 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.988 | 0.977 | 0.001 | 0.000 | 59.761 | 20.083 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 22.248 | 17.286 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.875 | 0.000 | 0.018 | 0.000 | -1 | 5 | NaN | NaN | 0.470 | 0.000 | 1.862 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.028 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.988 | 0.980 | 0.001 | 0.000 | 25.002 | 6.829 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 24.486 | 18.080 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.830 | 0.000 | 0.019 | 0.000 | 1 | 100 | NaN | NaN | 0.465 | 0.000 | 1.784 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.055 | 0.001 | 0.000 | 0.000 | 1 | 100 | 0.988 | 0.977 | 0.001 | 0.000 | 73.149 | 26.209 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.803 | 4.528 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.636 | 0.0 | 0.755 | 0.000 | k-means++ | NaN | 30 | NaN | 0.435 | 0.0 | 1.462 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.370 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.610 | 5.280 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.682 | 8.207 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.495 | 0.0 | 0.969 | 0.000 | random | NaN | 30 | NaN | 0.439 | 0.0 | 1.128 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.375 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.825 | 5.491 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.749 | 8.054 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.092 | 0.0 | 3.940 | 0.000 | k-means++ | NaN | 30 | NaN | 2.768 | 0.0 | 2.201 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.068 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.613 | 2.555 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.019 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.440 | 6.037 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.824 | 0.0 | 4.121 | 0.000 | random | NaN | 30 | NaN | 2.928 | 0.0 | 1.989 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 14.322 | 0.000 | random | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 6.191 | 2.588 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.019 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.553 | 6.714 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.079 | 0.0 | 0.040 | 0.000 | random | NaN | 20 | NaN | 0.035 | 0.0 | 2.286 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.185 | 0.000 | random | 0.001 | 20 | -0.000 | 0.001 | 0.0 | 2.488 | 0.364 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.846 | 6.722 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.250 | 0.0 | 0.013 | 0.000 | k-means++ | NaN | 20 | NaN | 0.088 | 0.0 | 2.830 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.190 | 0.000 | k-means++ | 0.001 | 20 | -0.001 | 0.001 | 0.0 | 2.442 | 0.471 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.137 | 6.180 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.220 | 0.0 | 0.726 | 0.000 | random | NaN | 20 | NaN | 0.146 | 0.0 | 1.513 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 6.296 | 0.000 | random | 0.312 | 20 | 0.227 | 0.001 | 0.0 | 1.955 | 0.306 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.041 | 5.070 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.631 | 0.0 | 0.254 | 0.000 | k-means++ | NaN | 20 | NaN | 0.357 | 0.0 | 1.769 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 6.056 | 0.000 | k-means++ | 0.296 | 20 | 0.318 | 0.001 | 0.0 | 2.124 | 0.274 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.710 | 4.164 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.068 | 0.0 | [-0.10660473] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.903 | 0.0 | 5.816 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.60662541] | 0.000 | NaN | NaN | NaN | NaN | 0.554 | 0.000 | 0.0 | 0.815 | 0.368 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.2273987] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.420 | 0.379 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.783 | 0.0 | [2.65793314] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.752 | 0.0 | 1.041 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [116.23383162] | 0.000 | NaN | NaN | NaN | NaN | 0.320 | 0.003 | 0.0 | 0.544 | 0.079 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [22.50074103] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.118 | 0.080 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.181 | 0.0 | 0.441 | 0.0 | NaN | NaN | NaN | 0.181 | 0.0 | 0.999 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 7.655 | 0.0 | NaN | NaN | 0.086 | 0.017 | 0.0 | 0.600 | 0.018 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.229 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.633 | 0.629 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.343 | 0.0 | 0.596 | 0.0 | NaN | NaN | NaN | 0.243 | 0.0 | 5.526 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 4.220 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.805 | 0.597 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.712 | 0.767 | See | See |